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Course Criteria
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1.00 Credits
An introduction to the art and practice of statistical consulting. Topics include active listening, ascertaining client knowledge level and ability, determining appropriate methods of analysis given limitations, and organizing and managing a consulting session. Prerequisite: graduate standing in statistics, 15 hours in statistics.
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4.00 Credits
Topics covered include probability theory, conditional probability, random variables, special distribution functions, functions of random variables, expectation, random samples, and limiting distributions. Prerequisite: MATH 2210, 3000 or MATH/STAT 4265.
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4.00 Credits
Topics covered include properties of a random sample, convergence concepts, principles of data reduction, methods of point estimation, evaluation of point estimators, as well as some interval estimation and hypothesis testing. Prerequisites: STAT5510.
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3.00 Credits
Topics covered include methods used in Bayesian, Likelihood, Frequentist inference; some methods for robust inference and some large sample theory as needed. Prerequisite: STAT 5520.
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3.00 Credits
Treats various limiting techniques which can be used to predict the behavior of statistics computed from large data sets. The characteristic function is used in deriving the law of large numbers and various forms of the central limit theorem, including the multivariate normal case. The central and noncentral chi-square distributions are derived as the probability law for certain statistics in the limit. Other topics discussed include modes of probabilistic convergence, speed of convergence, and large sample approximation procedures. Prerequisite: STAT 5510.
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3.00 Credits
A treatment of theory and application of ARIMA modeling of times series. Frequency domain analysis is also introduced. Additional topics will be selected from intervention analysis, transfer function (ARMAX) models, outlier analysis, vector ARIMA models, ARCH, GARCH, and state-space models, according to the interests and abilities of the class. Prerequisites: STAT 4015/5015, 4115 and 4265/5265.
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3.00 Credits
A theoretical approach to estimation and testing in the general linear model. Topics include: special linear algebra results for statistics, para-meterizations, estimability, least squares, best linear unbiased estimation, and testing linear hypotheses. Prerequisite: STAT 5630, 5520, MATH 4500.
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3.00 Credits
The subject matter includes derivation of multi-variate normal distributions, the Wishart, and related sampling distributions, multivariate estimation, confidence regions, and hypothesis testing are covered including topics as Hotelling's T squared, profile analysis, discriminate analysis, factor analysis, and cluster analysis. Prerequisite: STAT 4265, MATH 2250.
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3.00 Credits
This class of models based on exponential family distributions provides a unifying framework for linear normal models, models for categorical data and for survival analysis. Modeling and inference relies on familiarity with exponential family distributions, maximum likelihood inference and likelihood ratio tests. Prerequisite: STAT 5520 and STAT 5420.
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3.00 Credits
Consists of the theory of simple random sampling, stratified sampling, multistage sampling, and regression and ratio estimation. Recent developments in sampling are presented. Prerequisite: STAT 4265, STAT 4155/5155.
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